forecasting trachoma: control, elimination, or...
TRANSCRIPT
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Forecasting trachoma: control, elimination, or eradication?
Thomas M. Lietman Travis C. Porco
FI Proctor Foundation, UCSF April 2014
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Trachoma
• Causative agent Chlamydia trachomatis • Infection in children leads to blindness
later in life • Slated for elimination according to the
London Declaration • No nonhuman reservoir
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Severe TF/TI
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Important facts
• Ocular infection by C. trachomatis is easily cured with single-dose azithromycin (80-90% efficacy).
• No vaccine is currently available. • Clinical signs are unreliable in detecting
infection.
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Trachoma now
• WHO plan to stamp it out as a public health problem
• Surgery, antibiotics, face-washing, environment
• The SAFE program • Mass distribution of azithromycin the
cornerstone Schachter J, West SK, Mabey D, et al Lancet. 1999 Aug 21;354(9179):630-5
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TANA Trial
• TANA data • Annual treatment, biannual PCR • Prevalence estimate from 50 children • Use month 6 to simulate to month 12 • (A mass treatment occurs at month 12)
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TANA Trial
• Multi-armed clinical trial • Look first at two arms (24 villages):
– Baseline MDA at month 0 – Monitored at 12 and 24 months
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One round of MDA…
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Then what?
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State space (2)
0 1 2 N-1 N …
Infection
Recovery
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Standard model
• Stochastic SIS model:
dpidt
= N − i+1( )β i−1N −1"
#$
%
&'α"
#$$
%
&'' pi−1 t( )−
N − i( )β iN −1"
#$
%
&'α
+ iγ"
#$$
%
&'' pi t( )+
i+1( )γ( ) pi+1 t( )
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Elimination
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Elimination under MDA
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Scoring forecasts
• Probabilistic forecast—we produce the probability of the observed data at some time in the future
• Score the forecast by computing the quantity –log(L), where L is the probability of the data
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Comparing predictions
• Suppose the previous is the true distribution
• Predictions made from a distribution with R0=0.4 say are usually better (win 64% of the time, simulation N=10000), though have a lower expected score
• Simulation-based power studies for planning
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How well can we do?
• TANA data • Annual treatment • Biannual PCR based survey • Prevalence from 50 children at month 6 • Simulate to month 12 • (A mass treatment occurs at month 12)
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Trachoma
• Calibrate on months 6 to 12 and 18 to 24
• Initialize with known results at month 30 • Project to month 36 • Compare with known data
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Prediction
• Similarly for month 18 to month 24 • Assume conditional independence
given unknown random true coefficient in each village
• Transmission model serves as a simple nonlinear clustered regression model
• Use it to forecast month 36 from month 30 using posterior mode for estimated transmission coefficient
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Notes
• Note 1: true forecast score is computed from summing the probability in a sample given the true number infected times the probability of each true number infected
• Note 2: full analysis uses posterior density for village-specific transmission rate, instead of just posterior mode
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Assessing forecasts
• Ignorance score (minus loglikelihood) • -log(P(Y)), where Y is the observation,
and P is the probability of the observation as predicted by the model
• Others (proper linear score)
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Aside on loglikelihood
• What if the model is true, and I make forecasts from it. What is the expected ignorance score?
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Aside on loglikelihood
• What if the model is true, and I make forecasts from it. What is the expected ignorance score?
• Shannon entropy
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Next, look at 12 forecasts
• 12 villages • Train on first 2 years • Test on year 3
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Village 1
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Village 2
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Village 3
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Village 4
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Village 5
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Village 6
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Village 7
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Village 8
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Village 9
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Village 10
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Village 11
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Village 12
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Comment
• Much of our apparent forecast skill seems to be keeping the zero-prevalence villages at zero
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Summarizing
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Trachoma
• If these models are correct, there should be substantial stochastic variability at the small community level, though greater predictability for a group of communities.
• Striking outliers are possible even among theoretically identical villages
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What could we expect from this class of models?
• Simple models of this sort give a correlation of about 0.5 between one year and the next
• Observed correlation between baseline and 1 year for the 24 villages was 0.58
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Chasing ghosts
• Unpredictability of trachoma at the village level
• Long tail of the distribution • Expect transient local hot spots • The presence of a local hot spot does
NOT imply failure
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Importance
• Expect substantial variability even among identical villages
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Future directions
• Include clinical signs into model
• Include between-village differences (random effects)
dpi, j,kdt
= − γ j + ρk +λ j (i+ k)( ) pi, j,k +
λ j−1 i+1( ) pi+1, j−1,k +γ ( j +1)pi, j+1,k−1 +λ j−1(k +1)pi, j−1,k+1 + ρ(k +1)pi, j−1,k+1
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Funding That Man May See Bernard Osher Foundation Bodri Foundation Harper-Inglis Trust Peierls Foundation Jack and DeLoris Lange Foundation Research to Prevent Blindness International Trachoma Initiative/Pfizer NIAID: RO1-AI48789, R21-AI55752 NIGMS MIDAS NEI: U10-EY016214 Bill and Melinda Gates Foundation
With grateful acknowlegment